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I am implementing a research paper on image segmentation.
Following are the image segmentation steps which are to be done before training its network-

1.Following image normalization is used-

N(w, h) = I(w, h) − G(w, h),<br>

where G is the gaussian blur image with std dev = 60 and kernel size = 65*65 and I is the original image.
2. The images are normalized by subtracting the mean image computed over the training set, and dividing each pixel by the average standard deviation.
3. A validation split of 15% is selected.
4.Random crops of size 512 × 512 are extracted randomly out of the
original images ,We opt for a dynamic augmented data set, where training samples are generated randomly at the start of each mini-batch.
5. We artificially grow our data set by a factor of 8 through rotation
at 90, 180 and 270 degrees and horizontal flips.
6. We have implemented elastic deformation by sampling control points on
a regularly spaced 100 × 100 grid. Each control point has isotropic Gaussian noise added with σ = 20